On the impact of the galaxy window function on cosmological parameter estimation

Abstract

One important source of systematics in galaxy redshift surveys comes from the estimation of the galaxy window function. Up until now, the impact of the uncertainty in estimating the galaxy window function on parameter inference has not been properly studied. In this paper, we show that the uncertainty and the bias in estimating the galaxy window function will be salient for ongoing and next-generation galaxy surveys using a simulation-based approach. With a specific case study of cross-correlating Emission-line galaxies from the DESI Legacy Imaging Surveys and the Planck CMB lensing map, we show that neural network-based regression approaches to modelling the window function are superior in comparison to linear regression-based models. We additionally show that the definition of the galaxy overdensity estimator can impact the overall signal-to-noise of observed power spectra. Finally, we show that the additive biases coming from the window functions can significantly bias the modes of the inferred parameters and also degrade their precision. Thus, a careful understanding of the window functions will be essential to conduct cosmological experiments.

13 pages, 12 figures, complementary paper to an upcoming paper on Cross-Correlation of ELGs and Planck CMB lensing, accepted for publication in MNRAS

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Cited by in corpus (3)

• Constraining primordial non-Gaussianity from DESI quasar targets and Planck CMB lensing
• Measuring ${\sigma }_8$ using DESI Legacy Imaging Surveys Emission-Line Galaxies and Planck CMB Lensing and the Impact of Dust on Parameter Inference
• Pair Counting without Binning -- A New Approach to Correlation Functions in Clustering Statistics